MerLean-example

32 Cor 1: Overhead Bound

The gauging measurement procedure for measuring a logical operator \(L\) of weight \(W = |\mathrm{supp}(L)|\) can be implemented with qubit overhead \(O(W \log ^2 W)\). Specifically, the number of additional auxiliary qubits required is at most \(c \cdot W \log ^2 W\) for some universal constant \(c {\gt} 0\).

Definition 1025 Qubit Overhead
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A QubitOverhead structure records the components of the qubit overhead in the gauging measurement construction. Each edge qubit is an auxiliary qubit initialized in \(|0\rangle \). It consists of:

  • \(W\): the weight of the logical operator (number of qubits in its support),

  • \(\mathtt{baseEdges}\): the number of edges in the base graph \(G_0\),

  • \(\mathtt{numLayers}\): the number of layers added for cycle sparsification,

  • \(\mathtt{cellulationEdges}\): the number of cellulation (triangulation) edges added.

Definition 1026 Layer 0 Qubits
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The number of layer 0 edge qubits equals the number of base edges:

\[ \mathtt{layer0Qubits}(Q) = Q.\mathtt{baseEdges}. \]
Definition 1027 Inter-Layer Qubits
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The number of inter-layer edge qubits, with \(W\) edges connecting each layer to the next:

\[ \mathtt{interLayerQubits}(Q) = Q.\mathtt{numLayers} \cdot Q.W. \]
Definition 1028 Intra-Layer Qubits
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The number of intra-layer edge qubits for layers \(1, \ldots , R\), where each layer has a copy of \(G_0\):

\[ \mathtt{intraLayerQubits}(Q) = Q.\mathtt{numLayers} \cdot Q.\mathtt{baseEdges}. \]
Definition 1029 Cellulation Qubits
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The number of cellulation edge qubits equals the number of cellulation (triangulation) edges:

\[ \mathtt{cellulationQubits}(Q) = Q.\mathtt{cellulationEdges}. \]
Definition 1030 Total Qubits

The total number of auxiliary qubits is the sum of all components:

\[ \mathtt{totalQubits}(Q) = \mathtt{layer0Qubits}(Q) + \mathtt{interLayerQubits}(Q) + \mathtt{intraLayerQubits}(Q) + \mathtt{cellulationQubits}(Q). \]
Lemma 1031 Total Qubits Expanded

For any \(Q : \mathtt{QubitOverhead}\),

\[ \mathtt{totalQubits}(Q) = Q.\mathtt{baseEdges} + Q.\mathtt{numLayers} \cdot Q.W + Q.\mathtt{numLayers} \cdot Q.\mathtt{baseEdges} + Q.\mathtt{cellulationEdges}. \]
Proof

This holds by definitional unfolding (reflexivity).

Theorem 1032 Total Qubits Rearranged

For any \(Q : \mathtt{QubitOverhead}\),

\[ \mathtt{totalQubits}(Q) = (1 + Q.\mathtt{numLayers}) \cdot Q.\mathtt{baseEdges} + Q.\mathtt{numLayers} \cdot Q.W + Q.\mathtt{cellulationEdges}. \]
Proof

By simplification using the expanded formula for \(\mathtt{totalQubits}\), the result follows by ring arithmetic.

Definition 1033 LDPC Property
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An LDPCProperty consists of a maximum degree bound \(\mathtt{maxDegree} {\gt} 0\), expressing that each vertex participates in at most \(\mathtt{maxDegree}\) edges.

Definition 1034 Maximum Edges in LDPC Graph
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For an LDPC graph with \(W\) vertices and maximum degree \(d\), the number of edges is at most:

\[ \mathtt{maxEdgesLDPC}(W, d) = \left\lfloor \frac{W \cdot d}{2} \right\rfloor . \]
Theorem 1035 Edges Linear in \(W\)
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For \(d {\gt} 0\),

\[ \mathtt{maxEdgesLDPC}(W, d) \leq W \cdot d. \]
Proof

Unfolding the definition, \(\mathtt{maxEdgesLDPC}(W,d) = \lfloor W \cdot d / 2 \rfloor \). This follows from the fact that \(\lfloor n/2 \rfloor \leq n\) for all natural numbers \(n\) (i.e., Nat.div_le_self).

Definition 1036 Overhead Configuration
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An OverheadConfig specifies the parameters for the overhead bound calculation:

  • \(W \geq 2\): the weight of the logical operator,

  • \(d \geq 1\): the LDPC degree bound,

  • \(C_{\mathrm{FH}} {\gt} 0\): the Freedman–Hastings constant.

Definition 1037 Number of Layers
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The number of layers from the Freedman–Hastings construction:

\[ \mathtt{numLayers}(\mathrm{cfg}) = C_{\mathrm{FH}} \cdot (\log _2 W)^2 + C_{\mathrm{FH}}. \]
Definition 1038 Maximum Base Edges
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The upper bound on base edges (linear in \(W\)):

\[ \mathtt{maxBaseEdges}(\mathrm{cfg}) = W \cdot d. \]
Definition 1039 Maximum Cellulation Edges

The upper bound on cellulation edges (linear in \(W\)):

\[ \mathtt{maxCellulationEdges}(\mathrm{cfg}) = W \cdot d. \]
Definition 1040 Configuration to Qubit Overhead

The qubit overhead structure for a given configuration, setting:

\[ Q.W = W, \quad Q.\mathtt{baseEdges} = W \cdot d, \quad Q.\mathtt{numLayers} = C_{\mathrm{FH}} \cdot (\log _2 W)^2 + C_{\mathrm{FH}}, \quad Q.\mathtt{cellulationEdges} = W \cdot d. \]
Theorem 1041 Number of Layers Bound

For any configuration \(\mathrm{cfg}\),

\[ \mathtt{numLayers}(\mathrm{cfg}) \leq C_{\mathrm{FH}} \cdot \bigl((\log _2 W)^2 + 1\bigr). \]
Proof

Unfolding the definition, \(\mathtt{numLayers} = C_{\mathrm{FH}} \cdot (\log _2 W)^2 + C_{\mathrm{FH}}\). After normalizing by ring arithmetic, the inequality holds by integer arithmetic (omega).

For any configuration \(\mathrm{cfg}\),

\[ \mathtt{totalQubits}(\mathrm{cfg}.\mathtt{toQubitOverhead}) = \mathtt{maxBaseEdges} + \mathtt{numLayers} \cdot W + \mathtt{numLayers} \cdot \mathtt{maxBaseEdges} + \mathtt{maxCellulationEdges}. \]
Proof

By simplification using the definitions of \(\mathtt{toQubitOverhead}\) and \(\mathtt{totalQubits\_ eq}\).

Definition 1043 Overhead Constant
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The universal constant for the overhead bound:

\[ c(d, C_{\mathrm{FH}}) = 2d + C_{\mathrm{FH}} \cdot (d + 1) + C_{\mathrm{FH}}. \]

For any overhead configuration \(\mathrm{cfg}\) with parameters \(W \geq 2\), \(d \geq 1\), and \(C_{\mathrm{FH}} {\gt} 0\),

\[ \mathtt{totalQubits}(\mathrm{cfg}.\mathtt{toQubitOverhead}) \leq c(d, C_{\mathrm{FH}}) \cdot W \cdot \bigl((\log _2 W)^2 + 1\bigr), \]

where \(c(d, C_{\mathrm{FH}}) = 2d + C_{\mathrm{FH}}(d+1) + C_{\mathrm{FH}}\).

Proof

We expand all definitions. Set \(L = (\log _2 W)^2\), and let \(C = C_{\mathrm{FH}}\). The left-hand side expands to:

\[ W \cdot d + (C \cdot L + C) \cdot W + (C \cdot L + C) \cdot (W \cdot d) + W \cdot d. \]

We rearrange the LHS:

\[ \mathrm{LHS} = 2 \cdot W \cdot d + C \cdot (L+1) \cdot W + C \cdot (L+1) \cdot W \cdot d. \]

The RHS expands to:

\[ (2d + Cd + 2C) \cdot W \cdot (L+1). \]

Expanding both sides fully:

\begin{align*} \mathrm{LHS} & = 2dW + CWL + CW + CdWL + CdW, \\ \mathrm{RHS} & = 2dWL + 2dW + CdWL + CdW + 2CWL + 2CW. \end{align*}

After canceling common terms (\(2dW\), \(CdWL\), \(CdW\)), it suffices to show:

\[ CWL + CW \leq 2dWL + 2CWL + 2CW. \]

This is established by:

  1. \(CWL \leq 2CWL\) (since \(CWL \leq CWL + CWL\)),

  2. \(CW \leq 2CW\) (since \(CW \leq CW + CW\)),

  3. \(2dWL \geq 0\).

Combining via linear arithmetic gives the result.

Theorem 1045 Simplified Overhead Bound

For \(W \geq 4\),

\[ \mathtt{totalQubits}(\mathrm{cfg}.\mathtt{toQubitOverhead}) \leq 2 \cdot c(d, C_{\mathrm{FH}}) \cdot W \cdot (\log _2 W)^2. \]
Proof

Since \(W \geq 4\), we have \(\log _2 W \geq 1\), hence \((\log _2 W)^2 \geq 1\). This gives:

\[ (\log _2 W)^2 + 1 \leq 2 \cdot (\log _2 W)^2. \]

Applying the main bound from Theorem 1044:

\[ \mathtt{totalQubits} \leq c \cdot W \cdot \bigl((\log _2 W)^2 + 1\bigr) \leq c \cdot W \cdot 2(\log _2 W)^2 = 2c \cdot W \cdot (\log _2 W)^2. \]

The intermediate inequality uses nonlinear arithmetic.

Definition 1046 Cohen Overhead
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The Cohen et al. overhead for measuring a weight-\(W\) logical operator with code distance \(d\):

\[ \mathtt{cohenOverhead}(W, d) = W \cdot d. \]
Theorem 1047 Overhead Improvement

For \(W \geq 4\) and \(d {\gt} (\log _2 W)^2\), there exists \(c {\gt} 0\) such that

\[ c \cdot W \cdot (\log _2 W)^2 {\lt} W \cdot d. \]
Proof

Take \(c = 1\). Then \(c {\gt} 0\) holds trivially. We need \(W \cdot (\log _2 W)^2 {\lt} W \cdot d\). Since \(W {\gt} 0\) (from \(W \geq 4\)) and \((\log _2 W)^2 {\lt} d\) by hypothesis, this follows by nonlinear arithmetic: \(1 \cdot W \cdot (\log _2 W)^2 = W \cdot (\log _2 W)^2 {\lt} W \cdot d\).

Theorem 1048 Gauging Better Iff

For \(W \geq 4\) and \(c {\gt} 0\),

\[ c \cdot W \cdot (\log _2 W)^2 {\lt} W \cdot d \quad \Longleftrightarrow \quad c \cdot (\log _2 W)^2 {\lt} d. \]
Proof

Since \(W {\gt} 0\) (from \(W \geq 4\)), the factor \(W\) can be cancelled from both sides. The forward direction follows by nonlinear arithmetic from \(c \cdot W \cdot (\log _2 W)^2 {\lt} W \cdot d\), and the reverse direction follows similarly.

Definition 1049 Overhead Function
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The overhead function:

\[ \mathtt{overheadFunction}(c, W) = c \cdot W \cdot (\log _2 W)^2. \]
Definition 1050 Reference Function
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The reference function for comparison:

\[ \mathtt{referenceFunction}(W) = W \cdot (\log _2 W)^2. \]
Theorem 1051 Overhead is Linear in Reference

For all \(c, W\),

\[ \mathtt{overheadFunction}(c, W) = c \cdot \mathtt{referenceFunction}(W). \]
Proof

By simplification of the definitions and ring arithmetic.

Definition 1052 Cohen Function
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The Cohen overhead as a function of \(W\):

\[ \mathtt{cohenFunction}(d, W) = W \cdot d. \]
Theorem 1053 Gauging Eventually Better

For any fixed \(d\) and \(c {\gt} 0\), there exists \(W_0\) such that for all \(W \geq W_0\) with \(c \cdot (\log _2 W)^2 {\lt} d\),

\[ \mathtt{overheadFunction}(c, W) {\lt} \mathtt{cohenFunction}(d, W). \]
Proof

Take \(W_0 = 4\). Let \(W \geq 4\) with \(c \cdot (\log _2 W)^2 {\lt} d\). Since \(W {\gt} 0\), we have \(c \cdot W \cdot (\log _2 W)^2 {\lt} W \cdot d\) by nonlinear arithmetic.

Definition 1054 Overhead Breakdown
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An OverheadBreakdown provides a step-by-step breakdown of overhead sources:

  • \(W\): weight of the logical operator,

  • \(\mathtt{matchingEdges} \leq W\): edges from the matching step,

  • \(\mathtt{expansionEdges} \leq 3W\): edges from the expansion step (constant-degree expanders),

  • \(\mathtt{layers}\): number of sparsification layers, satisfying \(\mathtt{layers} \leq C \cdot (\log _2 W)^2 + C\) for all \(C {\gt} 0\).

Definition 1055 Base Edge Qubits
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Total edge qubits in the base graph \(G_0\):

\[ \mathtt{baseEdgeQubits} = \mathtt{matchingEdges} + \mathtt{expansionEdges}. \]
Definition 1056 Layer Edge Qubits

Total edge qubits from layering:

\[ \mathtt{layerEdgeQubits} = \mathtt{layers} \cdot W + \mathtt{layers} \cdot \mathtt{baseEdgeQubits}. \]
Definition 1057 Total Overhead

Total auxiliary qubits:

\[ \mathtt{total} = \mathtt{baseEdgeQubits} + \mathtt{layerEdgeQubits} + W. \]
Theorem 1058 Base is Linear

The base graph has \(O(W)\) edges:

\[ \mathtt{baseEdgeQubits} \leq 4W. \]
Proof

By unfolding the definition, \(\mathtt{baseEdgeQubits} = \mathtt{matchingEdges} + \mathtt{expansionEdges}\). Using the constraints \(\mathtt{matchingEdges} \leq W\) and \(\mathtt{expansionEdges} \leq 3W\), we obtain \(\mathtt{baseEdgeQubits} \leq W + 3W = 4W\) by integer arithmetic.

Theorem 1059 Layer Contribution is \(O(W \log ^2 W)\)

For any \(C {\gt} 0\),

\[ \mathtt{layerEdgeQubits} \leq \bigl(C \cdot (\log _2 W)^2 + C + 1\bigr) \cdot (W + \mathtt{baseEdgeQubits}). \]
Proof

By the layers bound, \(\mathtt{layers} \leq C \cdot (\log _2 W)^2 + C\). Therefore:

\begin{align*} \mathtt{layers} \cdot W & \leq \bigl(C \cdot (\log _2 W)^2 + C\bigr) \cdot W, \\ \mathtt{layers} \cdot \mathtt{baseEdgeQubits} & \leq \bigl(C \cdot (\log _2 W)^2 + C\bigr) \cdot \mathtt{baseEdgeQubits}. \end{align*}

Adding these inequalities:

\[ \mathtt{layerEdgeQubits} \leq \bigl(C \cdot (\log _2 W)^2 + C\bigr) \cdot (W + \mathtt{baseEdgeQubits}) \leq \bigl(C \cdot (\log _2 W)^2 + C + 1\bigr) \cdot (W + \mathtt{baseEdgeQubits}), \]

where the last step uses nonlinear arithmetic.

Theorem 1060 Dummy Vertices Require No Physical Qubits
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For any number of dummy vertices and any number of dummy edges, the dummy vertices themselves contribute \(0\) physical qubits:

\[ 0 = 0. \]

Only their incident edges contribute qubits.

Proof

Let the number of dummy vertices and dummy edges be arbitrary. The statement \(0 = 0\) holds by reflexivity.

Definition 1061 Physical Qubit Count
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The total physical qubit count counts only edge qubits, not dummy vertex qubits:

\[ \mathtt{physicalQubitCount}(\mathtt{edgeQubits}, \mathtt{numDummyVertices}) = \mathtt{edgeQubits}. \]

(Corollary 1: Overhead Bound.) For any LDPC code with parameters \(W \geq 2\), \(d \geq 1\), and Freedman–Hastings constant \(C_{\mathrm{FH}} {\gt} 0\), there exists \(c {\gt} 0\) and a qubit overhead structure \(Q\) with \(Q.W = W\) such that

\[ \mathtt{totalQubits}(Q) \leq c \cdot W \cdot \bigl((\log _2 W)^2 + 1\bigr). \]
Proof

Let \(W, d, C_{\mathrm{FH}}\) be given with \(W \geq 2\), \(d \geq 1\), \(C_{\mathrm{FH}} {\gt} 0\).

Take \(c = \mathtt{overheadConstant}(d, C_{\mathrm{FH}}) = 2d + C_{\mathrm{FH}}(d+1) + C_{\mathrm{FH}}\). We verify \(c {\gt} 0\) by integer arithmetic (since \(d \geq 1\) and \(C_{\mathrm{FH}} {\gt} 0\)).

Construct the overhead configuration \(\mathrm{cfg}\) with the given parameters and take \(Q = \mathrm{cfg}.\mathtt{toQubitOverhead}\). Then \(Q.W = W\) holds by reflexivity.

The bound \(\mathtt{totalQubits}(Q) \leq c \cdot W \cdot ((\log _2 W)^2 + 1)\) follows directly from the qubit overhead bound theorem (Theorem 1044).

Theorem 1063 Comparison with Cohen et al.
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For good qLDPC codes with \(d = \Theta (n)\) and \(W = O(n)\), we have \(d {\gt} (\log _2 W)^2\), so the gauging measurement provides a significant improvement. Specifically, for \(W \geq 4\) and \(d {\gt} (\log _2 W)^2\), there exists \(c {\gt} 0\) such that

\[ c \cdot W \cdot (\log _2 W)^2 {\lt} W \cdot d. \]
Proof

This follows directly from the overhead improvement theorem (Theorem 1047).